code: def create_text_streamer(
      tokenizer: PreTrainedTokenizer,
  ) -> TextStreamer:
      """Creates a TextStreamer object for streaming text to stdout during generation."""
      return TextStreamer(tokenizer=tokenizer, skip_prompt=True)
  
imports: import copy
  import logging
  import tempfile
  from typing import Dict, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn.functional
  import transformers
  from bitsandbytes.nn.modules import Embedding
  from packaging import version
  from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, TextStreamer
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.schema.trainer import LLMTrainerConfig
  from ludwig.utils.error_handling_utils import default_retry
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.model_utils import find_embedding_layer_with_path
label_desc: Use image node_id 15 for calling a global function with example usage: create_text_streamer(tokenizer) and returns: TextStreamer